Molecular docking approaches for drug discovery are used for predicting ligand binding modes, ligand binding affinities, and for computational screening to identify new specific ligands for biological targets of pharmaceutical relevance. Current approaches are characterized by high inter-target variability and sharp limitations in ability to make accurate predictions for new ligands that are structurally different from known ones. Approaches that model protein flexibility directly or approach simulation-level detail in complex molecular systems have shown some success. However, they are generally applicable only in low throughput and in cases where high-quality experimentally determined protein target structures are available. This is in part why "me-too" drugs dominate the pharmaceutical marketplace and development pipeline. Such drugs generally bring much less pharmacological novelty to patient treatment than structurally novel therapeutics. We propose an integrated set of methods for molecular docking that treats protein flexibility in a serious manner, is computationally efficient enough for wide use, and which offers the opportunity to effectively use docking in cases where few experimental structures exist for a biological target of interest. Our recent work has established an approach to treating protein flexibility in docking that addresses large protein movements by considering multiple experimental structures and small ligand-dependent movements by protein/ligand complex relaxation beginning from many putative ligand dockings. We have also established an approach for de novo protein pocket induction that constructs a binding site based solely on ligand binding data that is capable of making accurate predictions of binding geometry and binding affinity for structurally novel ligands. Our proposed work will combine these approaches. In cases where protein structural information is available, experimentally determined structures will undergo additional sampling, followed by refinement of the binding pockets based on ligand binding data in order to improve the predictions obtained from docking based upon our existing methods developed for de novo pocket construction. In addition to data-driven pocket refinement, the proposed effort requires improvement in our scoring functions for docking, taking into account vastly more data and explicit modeling of protein flexibility and of the unbound states of proteins and ligands. We will also put significant effort into algorithmic improvements that will result in typical run-times on common single-processor hardware of several minutes per ligand to yield predictions of binding geometry and affinity. We believe that a widely applicable, genuinely predictive, and computationally tractable modeling approach to docking will substantially improve drug discovery in practice. These methods will facilitate identification of novel lead compounds from directed lead optimization and computational screening exercises.

Public Health Relevance

Me-too drugs dominate the pharmaceutical marketplace and development pipeline. A shift toward novelty can be brought about if we can show that computational approaches for predicting the biological activity of small-molecules are able to make accurate predictions for structurally novel molecules. This requires significant advances over current methods for cases where a structure of the protein target is known (e.g. HIV pro- tease) and where one is not (e.g. the histamine receptor). We propose an integrated set of methods for molecular docking that treats protein flexibility in a serious manner, is computationally efficient enough for wide use, and which offers the opportunity to effectively use docking in cases where few experimental structures exist for a biological target of interest. Our proposed combines docking approaches with data-driven approaches that make use of ligand binding data. We will use ligand binding data (which is always available to some degree in drug discovery) to improve the predictions obtained from docking in cases where protein structures are known and can be expanded through sampling. These methods rely on a novel approach to machine learning, developed by our lab. We believe that a widely applicable, genuinely predictive, and computationally tractable modeling approach to docking will substantially improve drug discovery in practice. These methods will facilitate identification of novel lead compounds from directed drug lead optimization and computational screening exercises. Our proposed work is to make substantial improvements in molecular docking, augmented significantly by protein flexibility and ligand-based refinement.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM070481-07
Application #
8304944
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
2005-07-01
Project End
2014-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
7
Fiscal Year
2012
Total Cost
$307,013
Indirect Cost
$100,509
Name
University of California San Francisco
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Spitzer, Russell; Cleves, Ann E; Varela, Rocco et al. (2014) Protein function annotation by local binding site surface similarity. Proteins 82:679-94
Yera, Emmanuel R; Cleves, Ann E; Jain, Ajay N (2014) Prediction of off-target drug effects through data fusion. Pac Symp Biocomput :160-71
Jain, Ajay N; Cleves, Ann E (2012) Does your model weigh the same as a duck? J Comput Aided Mol Des 26:57-67
Yera, Emmanuel R; Cleves, Ann E; Jain, Ajay N (2011) Chemical structural novelty: on-targets and off-targets. J Med Chem 54:6771-85
Neelarapu, Raghupathi; Holzle, Denise L; Velaparthi, Subash et al. (2011) Design, synthesis, docking, and biological evaluation of novel diazide-containing isoxazole- and pyrazole-based histone deacetylase probes. J Med Chem 54:4350-64
Spitzer, Russell; Cleves, Ann E; Jain, Ajay N (2011) Surface-based protein binding pocket similarity. Proteins 79:2746-63
Jain, Ajay N (2010) QMOD: physically meaningful QSAR. J Comput Aided Mol Des 24:865-78
Langham, James J; Cleves, Ann E; Spitzer, Russell et al. (2009) Physical binding pocket induction for affinity prediction. J Med Chem 52:6107-25
Jain, Ajay N (2009) Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des 23:355-74
Langham, James J; Jain, Ajay N (2008) Accurate and interpretable computational modeling of chemical mutagenicity. J Chem Inf Model 48:1833-9

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